In the past, decisions frequently had to be made on minimal amounts of available data. Information traveled slowly, and the scope of the information available was within a scale that could be considered by a human mind. Frequently, the greatest problem facing a decision-maker was a paucity of information. Advances in information gathering and transmittal technologies have reversed this trend, making it easier to gather large amounts of information pertaining to a particular problem. A major task facing modern day decision-makers is filtering and organizing the received information into a useful form. But perhaps the most significant challenge is determining how to process the data, accounting for the inherent uncertainties in the data such as conflicts, false information, ambiguities, errors, measurement biases, etc.
While automated classification and decision-making systems have become increasingly sophisticated, the human mind still outperforms automated systems on any real-world tasks that require judgment. One limitation of human decision-making, however, is the inability of human beings to consider a large number of factors simultaneously. Another limitation experienced by human decision-makers is their inability to correctly analyze information with inherent uncertainties or worse, uncertainty induced by processing. Decision-makers often find it difficult to combine large amounts of evidence mentally, and the human tendency is to postpone risky decisions when data are incomplete, or jump to conclusions and refuse to consider conflicting data. Accordingly, automated methods of organizing, combining, correlating, and displaying data that account for the uncertainties in the data can greatly aid human decision-makers.
In attempting to structure and filter the data presented to a human decision-maker, an unfortunate tendency of many decision support systems is to oversimplify the situation presented to the decision-maker. While any real-world decision must consider many different types of uncertainty, this uncertainty is often hidden from the decision-maker by eliminating the context of the information or presenting a single uncertainty value by thresholding. This leaves the decision-maker without explicit information about the uncertainty regarding each “fact” presented as relevant to the pending decision. Implicit information, data thresholding, and loss of context can force the decision-maker to guess about such uncertainty in arriving at a decision or give the decision maker a false sense of security about the situation and their decision. Unfortunately, this can result in sub-optimal decisions, because vital information has in effect been hidden from the decision-maker by the automation system. A parallel situation pertains with regard to automated tools that perform analysis of a situation, and make decisions or recommendations—current practice tends to “hide” the full range of interpretations of the input data, leading to inferior and even inaccurate decisions and recommendations.